{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "\n",
    "from helper import linear_regression as lr\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load in raw data, not intercept term\n",
    "X, y, Xval, yval, Xtest, ytest = lr.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# create polynomial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>f3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-15.936758</td>\n",
       "      <td>253.980260</td>\n",
       "      <td>-4047.621971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-29.152979</td>\n",
       "      <td>849.896197</td>\n",
       "      <td>-24777.006175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.189549</td>\n",
       "      <td>1309.683430</td>\n",
       "      <td>47396.852168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.492187</td>\n",
       "      <td>1405.664111</td>\n",
       "      <td>52701.422173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-48.058829</td>\n",
       "      <td>2309.651088</td>\n",
       "      <td>-110999.127750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-8.941458</td>\n",
       "      <td>79.949670</td>\n",
       "      <td>-714.866612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.307793</td>\n",
       "      <td>234.328523</td>\n",
       "      <td>3587.052500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-34.706266</td>\n",
       "      <td>1204.524887</td>\n",
       "      <td>-41804.560890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.389154</td>\n",
       "      <td>1.929750</td>\n",
       "      <td>2.680720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-44.383760</td>\n",
       "      <td>1969.918139</td>\n",
       "      <td>-87432.373590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.013502</td>\n",
       "      <td>49.189211</td>\n",
       "      <td>344.988637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>22.762749</td>\n",
       "      <td>518.142738</td>\n",
       "      <td>11794.353058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           f1           f2             f3\n",
       "0  -15.936758   253.980260   -4047.621971\n",
       "1  -29.152979   849.896197  -24777.006175\n",
       "2   36.189549  1309.683430   47396.852168\n",
       "3   37.492187  1405.664111   52701.422173\n",
       "4  -48.058829  2309.651088 -110999.127750\n",
       "5   -8.941458    79.949670    -714.866612\n",
       "6   15.307793   234.328523    3587.052500\n",
       "7  -34.706266  1204.524887  -41804.560890\n",
       "8    1.389154     1.929750       2.680720\n",
       "9  -44.383760  1969.918139  -87432.373590\n",
       "10   7.013502    49.189211     344.988637\n",
       "11  22.762749   518.142738   11794.353058"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.poly_features(X, power=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# prepare polynomial regression data\n",
    "1. expand feature with power = 8, or any power you want to use \n",
    "2. Apply **normalization** to combat $x^n$ situation\n",
    "3. don't forget intercept term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.00000000e+00,  -3.62140776e-01,  -7.55086688e-01,\n",
       "          1.82225876e-01,  -7.06189908e-01,   3.06617917e-01,\n",
       "         -5.90877673e-01,   3.44515797e-01,  -5.08481165e-01],\n",
       "       [  1.00000000e+00,  -8.03204845e-01,   1.25825266e-03,\n",
       "         -2.47936991e-01,  -3.27023420e-01,   9.33963187e-02,\n",
       "         -4.35817606e-01,   2.55416116e-01,  -4.48912493e-01],\n",
       "       [  1.00000000e+00,   1.37746700e+00,   5.84826715e-01,\n",
       "          1.24976856e+00,   2.45311974e-01,   9.78359696e-01,\n",
       "         -1.21556976e-02,   7.56568484e-01,  -1.70352114e-01]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_poly, Xval_poly, Xtest_poly= lr.prepare_poly_data(X, Xval, Xtest, power=8)\n",
    "X_poly[:3, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot learning curve\n",
    "> again, first we don't apply regularization $\\lambda=0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5vvnmGzp06ECFChUoU6YMjRs35umnnwZg0aJFtGrVCmMM/fr1w+VyERAQwIcffljAd6j4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\nDREREdFKioaIiIhoJUVDREREtPI/7oPOffMah6UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10542ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr.plot_learning_curve(X_poly, y, Xval_poly, yval, l=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "as you can see the training cost is too low to be true. This is **over fitting**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# try $\\lambda=1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KzKzEzH5nZlmd+UGkc0I5IfU0iIhIqzrS05AFfAhcAzQZo2dm/wVcC1wJHAPs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\nRfwzsKriAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10542eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr.plot_learning_curve(X_poly, y, Xval_poly, yval, l=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "training cost increat a little bit, not 0 anymore.  \n",
    "Say we alleviate **over fitting** a little bit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# try $\\lambda=100$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5RyaEEsIRJDQIu7PZoEfrhrT2b01UrFyiEGXruuvMv8E77zQTQvXv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\ntWQCQ/QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0ae128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr.plot_learning_curve(X_poly, y, Xval_poly, yval, l=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "too much regularization.  \n",
    "back to **underfit** situation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# find the best $\\lambda$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l_candidate = [0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10]\n",
    "training_cost, cv_cost = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for l in l_candidate:\n",
    "    res = lr.linear_regression_np(X_poly, y, l)\n",
    "    \n",
    "    tc = lr.cost(res.x, X_poly, y)\n",
    "    cv = lr.cost(res.x, Xval_poly, yval)\n",
    "    \n",
    "    training_cost.append(tc)\n",
    "    cv_cost.append(cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11a410ef0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PJWTY0sXoi0z6fRLtQtvZ9LrOP6KRGGd2CUIIIbLo+nX46CP47DMoWxZWr4a2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\neeOgYWZmZnnjoGFmZmZ58//7RCdzZdpoxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0a9320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(l_candidate, training_cost, label='training')\n",
    "plt.plot(l_candidate, cv_cost, label='cross validation')\n",
    "plt.legend(loc=2)\n",
    "\n",
    "plt.xlabel('lambda')\n",
    "\n",
    "plt.ylabel('cost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best cv I got from all those candidates\n",
    "l_candidate[np.argmin(cv_cost)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test cost(l=0) = 9.982275423899827\n",
      "test cost(l=0.001) = 10.96403493885111\n",
      "test cost(l=0.003) = 11.264458872657682\n",
      "test cost(l=0.01) = 10.880094765571297\n",
      "test cost(l=0.03) = 10.022266931655883\n",
      "test cost(l=0.1) = 8.632063139750382\n",
      "test cost(l=0.3) = 7.336640278544401\n",
      "test cost(l=1) = 7.466289435179381\n",
      "test cost(l=3) = 11.64393193727906\n",
      "test cost(l=10) = 27.715080291767972\n"
     ]
    }
   ],
   "source": [
    "# use test data to compute the cost\n",
    "for l in l_candidate:\n",
    "    theta = lr.linear_regression_np(X_poly, y, l).x\n",
    "    print('test cost(l={}) = {}'.format(l, lr.cost(theta, Xtest_poly, ytest)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "turns out $\\lambda = 0.3$ is even better choice XD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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